DOCSLIB.ORG

Whole-Genome and RNA Sequencing Reveal Variation and Transcriptomic Coordination in the Developing Human Prefrontal Cortex

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Whole-Genome and RNA Sequencing Reveal Variation and Transcriptomic Coordination in the Developing Human Prefrontal Cortex UCSF UC San Francisco Previously Published Works Title Whole-Genome and RNA Sequencing Reveal Variation and Transcriptomic Coordination in the Developing Human Prefrontal Cortex. Permalink https://escholarship.org/uc/item/8j92s7z1 Journal Cell reports, 31(1) ISSN 2211-1247 Authors Werling, Donna M Pochareddy, Sirisha Choi, Jinmyung et al. Publication Date 2020-04-01 DOI 10.1016/j.celrep.2020.03.053 Peer reviewed eScholarship.org Powered by the California Digital Library University of California HHS Public Access Author manuscript Author ManuscriptAuthor Manuscript Author Cell Rep Manuscript Author . Author manuscript; Manuscript Author available in PMC 2020 June 15. Published in final edited form as: Cell Rep. 2020 April 07; 31(1): 107489. doi:10.1016/j.celrep.2020.03.053. Whole-Genome and RNA Sequencing Reveal Variation and Transcriptomic Coordination in the Developing Human Prefrontal Cortex Donna M. Werling1,2,32, Sirisha Pochareddy3,32, Jinmyung Choi3,32, Joon-Yong An1,4,5,32, Brooke Sheppard1, Minshi Peng6, Zhen Li3,7, Claudia Dastmalchi1, Gabriel Santpere3,8, André M.M. Sousa3, Andrew T.N Tebbenkamp3, Navjot Kaur3, Forrest O. Gulden3, Michael S. Breen9,10,11,12, Lindsay Liang1, Michael C. Gilson1, Xuefang Zhao13,14,15, Shan Dong1, Lambertus Klei16, A. Ercument Cicek17,18, Joseph D. Buxbaum9,10,11,19, Homa Adle- Biassette20, Jean-Leon Thomas21,22, Kimberly A. Aldinger23,24, Diana R. O’Day25, Ian A. Glass25, Noah A. Zaitlen26, Michael E. Talkowski13,14,15, Kathryn Roeder6,18, Matthew W. State1,27, Bernie Devlin16, Stephan J. Sanders1,27,33,*, Nenad Sestan3,28,29,30,31,* 1Department of Psychiatry, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94158, USA 2Laboratory of Genetics, University of Wisconsin-Madison, Madison, WI 53706, USA 3Department of Neuroscience and Kavli Institute for Neuroscience, Yale School of Medicine, New Haven, CT 06510, USA 4Department of Integrated Biomedical and Life Science, Korea University, Seoul 02841, Republic of Korea 5School of Biosystem and Biomedical Science, College of Health Science, Korea University, Seoul 02841, Republic of Korea 6Department of Statistics and Data Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA 7Department of Neurosciences, University of California, San Diego, San Diego, CA 92093, USA This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). *Correspondence: [email protected] (S.J.S.), [email protected] (N.S.). AUTHOR CONTRIBUTIONS Conceptualization, K.R., M.W.S., B.D., S.J.S., and N.S.; Methodology, D.M.W., S.P., J.C., J.-Y.A., H.A.-B., J.-L.T., K.A.A., D.R.O., I.A.G., K.R., M.W.S., B.D., S.J.S., and N.S.; Software, D.M.W., J.-Y.A., C.D., L.L., M.C.G., and S.J.S.; Validation, D.M.W., S.P., J.C., J.-Y.A., C.D., S.D., B.D., S.J.S., and N.S.; Formal Analysis, D.M.W., S.P., J.C., J.-Y.A., B.S., M.P., G.S., M.S.B., L.K., A.E.C., B.D., and S.J.S.; Investigation, D.M.W., S.P., J.C., J.-Y.A., B.S., B.D., S.J.S., and N.S.; Resources, S.P., J.C., Z.L., C.D., A.M.M.S, A.T.N.T., N.K., F.O.G., L.L., M.C.G., X.Z., H.A.-B., J.-L.T., K.A.A., D.R.O., I.A.G., M.E.T., S.J.S., and N.S.; Data Curation, D.M.W., S.P., J.C., C.D., L.L., M.C.G., X.Z., S.D., and S.J.S.; Statistical analysis, D.M.W., J.C., J.-Y.A., B.S., M.P., M.S.B., B.D., and S.J.S.; Writing – Original Draft, D.M.W., S.P., J.C., J.-Y.A., B.S., B.D., S.J.S., and N.S.; Writing – Review & Editing, D.M.W., S.P., J.C., J.- Y.A., B.S., M.P., Z.L., C.D., G.S., A.M.M.S., A.T.N.T., N.K., F.O.G., M.S.B., L.L., M.C.G., X.Z., S.D., L.K., A.E.C., J.D.B., H.A.-B., J.-L.T., K.A.A., D.R.O., I.A.G., N.A.Z., M.E.T., K.R., M.W.S., B.D., S.J.S., and N.S.; Visualization, D.M.W., S.P., J.C., J.-Y.A., B.S., S.J.S., and N.S.; Supervision, J.D.B., N.A.Z., M.E.T., K.R., M.W.S., B.D., S.J.S., and N.S.; Project Administration, D.M.W., S.P., S.J.S., and N.S.; Funding Acquisition, K.R., M.W.S., B.D., S.J.S., and N.S. DECLARATION OF INTERESTS The authors declare no competing interests. SUPPLEMENTAL INFORMATION Supplemental Information can be found online at https://doi.org/10.1016/j.celrep.2020.03.053. Werling et al. Page 2 8Neurogenomics Group, Research Programme on Biomedical Informatics, Hospital del Mar Author ManuscriptAuthor Manuscript Author Manuscript Author Manuscript Author Medical Research Institute, Department of Experimental and Health Sciences, Universitat Pompeu Fabra, 08003 Barcelona, Catalonia, Spain 9Seaver Autism Center for Research and Treatment, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA 10Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA 11Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA 12Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA 13Center for Genomic Medicine and Department of Neurology, Massachusetts General Hospital, Boston, MA 02114, USA 14Department of Neurology, Harvard Medical School, Boston, MA 02115, USA 15Program in Medical and Population Genetics and Stanley Center for Psychiatric Research, Broad Institute, Cambridge, MA 02142, USA 16Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, USA 17Department of Computer Engineering, Bilkent University, Ankara 06800, Turkey 18Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA 15213, USA 19Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA 20Department of Pathology, Lariboisière Hospital, APHP, Biobank BB-0033-00064, and Université de Paris, 75006 Paris, France 21Department of Neurology, Yale University School of Medicine, New Haven, CT 06511, USA 22UMRS1127, Sorbonne Université, Institut du Cerveau et de la Moelle Épinière, 75013 Paris, France 23Center for Integrative Brain Research, Seattle Children’s Research Institute, Seattle, WA 98101, USA 24Brotman Baty Institute for Precision Medicine, Seattle, WA 98195, USA 25Department of Pediatrics, University of Washington, Seattle, WA 98105, USA 26Department of Medicine, University of California, San Francisco, San Francisco, CA 94158, USA 27Institute for Human Genetics, University of California, San Francisco, San Francisco, CA 94158, USA 28Department of Psychiatry, Yale University School of Medicine, New Haven, CT 06520, USA 29Department of Genetics, Yale University School of Medicine, New Haven, CT 06520, USA Cell Rep. Author manuscript; available in PMC 2020 June 15. Werling et al. Page 3 30Department of Comparative Medicine, Program in Integrative Cell Signaling and Neurobiology Author ManuscriptAuthor Manuscript Author Manuscript Author Manuscript Author of Metabolism, Yale School of Medicine, New Haven, CT 06510, USA 31Program in Cellular Neuroscience, Neurodegeneration, and Repair and Yale Child Study Center, Yale School of Medicine, New Haven, CT 06510, USA 32These authors contributed equally to this work 33Lead Contact SUMMARY Gene expression levels vary across developmental stage, cell type, and region in the brain. Genomic variants also contribute to the variation in expression, and some neuropsychiatric disorder loci may exert their effects through this mechanism. To investigate these relationships, we present BrainVar, a unique resource of paired whole-genome and bulk tissue RNA sequencing from the dorsolateral prefrontal cortex of 176 individuals across prenatal and postnatal development. Here we identify common variants that alter gene expression (expression quantitative trait loci [eQTLs]) constantly across development or predominantly during prenatal or postnatal stages. Both “constant” and “temporal-predominant” eQTLs are enriched for loci associated with neuropsychiatric traits and disorders and colocalize with specific variants. Expression levels of more than 12,000 genes rise or fall in a concerted late-fetal transition, with the transitional genes enriched for cell-type-specific genes and neuropsychiatric risk loci, underscoring the importance of cataloging developmental trajectories in understanding cortical physiology and pathology. In Brief Werling et al. analyze gene expression across the span of human cerebral cortical development and profile the trajectories of individual genes, coordinated groups of genes, and their relationships to disorders. Integration of genetic variation identifies quantitative trait loci that implicate specific genes in loci associated with neuropsychiatric traits and disorders. Graphical Abstract Cell Rep. Author manuscript; available in PMC 2020 June 15. Werling et al. Page 4 Author ManuscriptAuthor Manuscript Author Manuscript Author Manuscript Author INTRODUCTION The human nervous system develops slowly over several decades, starting during embryogenesis and extending postnatally through infancy, childhood, adolescence, and young adulthood (Keshavan et al., 2014; Shaw et al., 2010; Silbereis et al., 2016; Tau and Peterson, 2010). Over this time, myriads of functionally distinct cell types, circuits, and regions are formed (Hu et al., 2014; Lui et al., 2011; Silbereis et al., 2016). To produce distinct structures and circuits, neural cells are born in an immature state and undergo a variety of molecular and morphological changes as they differentiate, migrate, and establish circuits. Consequently, the characteristics of a given cell and brain region at a given time offer only a snapshot of organogenesis and brain function, necessitating consistent profiling across development.
Load more

Recommended publications
  • Bppart and Bpmax: RNA-RNA Interaction Partition Function and Structure Prediction for the Base Pair Counting Model
    BPPart and BPMax: RNA-RNA Interaction Partition Function and Structure Prediction for the Base Pair Counting Model Ali Ebrahimpour-Boroojeny, Sanjay Rajopadhye, and Hamidreza Chitsaz ∗ Department of Computer Science, Colorado State University Abstract A few elite classes of RNA-RNA interaction (RRI), with complex roles in cellular functions such as miRNA-target and lncRNAs in human health, have already been studied. Accordingly, RRI bioinfor- matics tools tailored for those elite classes have been proposed in the last decade. Interestingly, there are somewhat unnoticed mRNA-mRNA interactions in the literature with potentially drastic biological roles. Hence, there is a need for high-throughput generic RRI bioinformatics tools. We revisit our RRI partition function algorithm, piRNA, which happens to be the most comprehensive and computationally-intensive thermodynamic model for RRI. We propose simpler models that are shown to retain the vast majority of the thermodynamic information that piRNA captures. We simplify the energy model and instead consider only weighted base pair counting to obtain BPPart for Base-pair Partition function and BPMax for Base-pair Maximization which are 225 and 1350 faster ◦ × × than piRNA, with a correlation of 0.855 and 0.836 with piRNA at 37 C on 50,500 experimentally charac- terized RRIs. This correlation increases to 0.920 and 0.904, respectively, at 180◦C. − Finally, we apply our algorithm BPPart to discover two disease-related RNAs, SNORD3D and TRAF3, and hypothesize their potential roles in Parkinson's disease and Cerebral Autosomal Dominant Arteri- opathy with Subcortical Infarcts and Leukoencephalopathy (CADASIL). 1 Introduction Since mid 1990s with the advent of RNA interference discovery, RNA-RNA interaction (RRI) has moved to the spotlight in modern, post-genome biology.
    [Show full text]
  • Dynamic Clonal Progression in Xenografts of Acute Lymphoblastic Leukemia with Intrachromosomal Amplification of Chromosome 21
    Sinclair PB, Blair HH, Ryan SL, Buechler L, Cheng J, Clayton J, Hanna R, Hollern S, Hawking Z, Bashton M, Schwab CJ, Jones L, Russell LJ, Marr H, Carey P, Halsey C, Heidenreich O, Moorman AV, Harrison CJ. Dynamic clonal progression in xenografts of acute lymphoblastic leukemia with intrachromosomal amplification of chromosome 21. Haematologica 2018, https://doi.org/10.3324/haematol.2017.172304 Copyright: © 2018, Ferrata Storti Foundation. Authors will grant copyright of their article to the Ferrata Storti Foundation. No formal permission will be required to reproduce parts (tables or illustrations) of published papers, provided the source is quoted appropriately and reproduction has no commercial intent. Reproductions with commercial intent will require written permission and payment of royalties. Please contact the office for requests: [email protected] DOI link to article: https://doi.org/10.3324/haematol.2017.172304 Date deposited: 20/03/2018 This work is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported License Newcastle University ePrints - eprint.ncl.ac.uk Published Ahead of Print on February 15, 2018, as doi:10.3324/haematol.2017.172304. Copyright 2018 Ferrata Storti Foundation. Dynamic clonal progression in xenografts of acute lymphoblastic leukemia with intrachromosomal amplification of chromosome 21 by Paul B. Sinclair, Helen H. Blair, Sarra L. Ryan, Lars Buechler, Joanna Cheng, Jake Clayton, Rebecca Hanna, Shaun Hollern, Zoe Hawking, Matthew Bashton, Claire J. Schwab, Lisa Jones, Lisa J. Russell, Helen Marr, Peter Carey, Christina Halsey, Olaf Heidenreich, Anthony V. Moorman, and Christine J. Harrison Haematologica 2018 [Epub ahead of print] Citation: Paul B. Sinclair, Helen H.
    [Show full text]
  • Primepcr™Assay Validation Report
    PrimePCR™Assay Validation Report Gene Information Gene Name oligodendrocytic myelin paranodal and inner loop protein Gene Symbol OPALIN Organism Human Gene Summary Description Not Available Gene Aliases HTMP10, TMEM10, TMP10 RefSeq Accession No. NC_000010.10, NT_030059.13 UniGene ID Hs.12449 Ensembl Gene ID ENSG00000197430 Entrez Gene ID 93377 Assay Information Unique Assay ID qHsaCID0015438 Assay Type SYBR® Green Detected Coding Transcript(s) ENST00000371172, ENST00000393871, ENST00000393870, ENST00000419479, ENST00000536387 Amplicon Context Sequence GGGCCTCTTGTGCTCCCATCGTATTCTTCTCATGTGTGGGTGATCTCCTAGGATT CTCAGATATCTTGGGATTGTCATCAATTTCTGAAAT Amplicon Length (bp) 61 Chromosome Location 10:98105820-98108081 Assay Design Intron-spanning Purification Desalted Validation Results Efficiency (%) 99 R2 0.9996 cDNA Cq Target not expressed in universal RNA cDNA Tm (Celsius) Target not expressed in universal RNA gDNA Cq Target not expressed in universal RNA Specificity (%) No cross reactivity detected Information to assist with data interpretation is provided at the end of this report. Page 1/4 PrimePCR™Assay Validation Report OPALIN, Human Amplification Plot Amplification of cDNA generated from 25 ng of universal reference RNA Melt Peak Melt curve analysis of above amplification Standard Curve Standard curve generated using 20 million copies of template diluted 10-fold to 20 copies Page 2/4 PrimePCR™Assay Validation Report Products used to generate validation data Real-Time PCR Instrument CFX384 Real-Time PCR Detection System Reverse Transcription Reagent iScript™ Advanced cDNA Synthesis Kit for RT-qPCR Real-Time PCR Supermix SsoAdvanced™ SYBR® Green Supermix Experimental Sample qPCR Human Reference Total RNA Data Interpretation Unique Assay ID This is a unique identifier that can be used to identify the assay in the literature and online. Detected Coding Transcript(s) This is a list of the Ensembl transcript ID(s) that this assay will detect.
    [Show full text]
  • Combined Analysis of Genome-Wide Expression and Copy Number
    Fontanillo et al. BMC Genomics 2012, 13(Suppl 5):S5 http://www.biomedcentral.com/1471-2164/13/S5/S5 RESEARCH Open Access Combined analysis of genome-wide expression and copy number profiles to identify key altered genomic regions in cancer Celia Fontanillo1, Sara Aibar1, Jose Manuel Sanchez-Santos2, Javier De Las Rivas1* From X-meeting 2011 - International Conference on the Brazilian Association for Bioinformatics and Compu- tational Biology Florianópolis, Brazil. 12-15 October 2011 Abstract Background: Analysis of DNA copy number alterations and gene expression changes in human samples have been used to find potential target genes in complex diseases. Recent studies have combined these two types of data using different strategies, but focusing on finding gene-based relationships. However, it has been proposed that these data can be used to identify key genomic regions, which may enclose causal genes under the assumption that disease-associated gene expression changes are caused by genomic alterations. Results: Following this proposal, we undertake a new integrative analysis of genome-wide expression and copy number datasets. The analysis is based on the combined location of both types of signals along the genome. Our approach takes into account the genomic location in the copy number (CN) analysis and also in the gene expression (GE) analysis. To achieve this we apply a segmentation algorithm to both types of data using paired samples. Then, we perform a correlation analysis and a frequency analysis of the gene loci in the segmented CN regions and the segmented GE regions; selecting in both cases the statistically significant loci. In this way, we find CN alterations that show strong correspondence with GE changes.
    [Show full text]
  • Transcriptomic Characterization of MRI Contrast with Focus on the T1-W/T2-W Ratio in the Cerebral ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ Cortex
    bioRxiv preprint doi: https://doi.org/10.1101/196386; this version posted October 31, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Title: Transcriptomic characterization of MRI contrast with focus on the T1-w/T2-w ratio in the cerebral ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ cortex 1,2 3 1,4,5 Authors: Jacob Ritchie ,​ Spiro Pantazatos ,​ Leon French ​ ​​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ 1 Computational​ Neurobiology Lab, Campbell Family Mental Health Research Institute, Centre ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ for Addiction and Mental Health, Toronto, ON, Canada; 2 ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ Division​ of Engineering Science, University of Toronto, Toronto, ON, Canada; 3 ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ Department​ of Psychiatry, Columbia University, New York, NY, USA; 4 ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ Department​ of Psychiatry, University of Toronto; 5 ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ Institute​ of Medical Science, University of Toronto ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ Keywords: gene expression, molecular neuroanatomy, transcriptomics, myelin map, cortex ​ ​​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ Abstract Magnetic resonance (MR) images of the brain are of immense clinical and research utility. At the ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ atomic and subatomic levels, the sources of MR signals are well understood. However, at the ​ ​ ​
    [Show full text]
  • Detection of H3k4me3 Identifies Neurohiv Signatures, Genomic
    viruses Article Detection of H3K4me3 Identifies NeuroHIV Signatures, Genomic Effects of Methamphetamine and Addiction Pathways in Postmortem HIV+ Brain Specimens that Are Not Amenable to Transcriptome Analysis Liana Basova 1, Alexander Lindsey 1, Anne Marie McGovern 1, Ronald J. Ellis 2 and Maria Cecilia Garibaldi Marcondes 1,* 1 San Diego Biomedical Research Institute, San Diego, CA 92121, USA; [email protected] (L.B.); [email protected] (A.L.); [email protected] (A.M.M.) 2 Departments of Neurosciences and Psychiatry, University of California San Diego, San Diego, CA 92103, USA; [email protected] * Correspondence: [email protected] Abstract: Human postmortem specimens are extremely valuable resources for investigating trans- lational hypotheses. Tissue repositories collect clinically assessed specimens from people with and without HIV, including age, viral load, treatments, substance use patterns and cognitive functions. One challenge is the limited number of specimens suitable for transcriptional studies, mainly due to poor RNA quality resulting from long postmortem intervals. We hypothesized that epigenomic Citation: Basova, L.; Lindsey, A.; signatures would be more stable than RNA for assessing global changes associated with outcomes McGovern, A.M.; Ellis, R.J.; of interest. We found that H3K27Ac or RNA Polymerase (Pol) were not consistently detected by Marcondes, M.C.G. Detection of H3K4me3 Identifies NeuroHIV Chromatin Immunoprecipitation (ChIP), while the enhancer H3K4me3 histone modification was Signatures, Genomic Effects of abundant and stable up to the 72 h postmortem. We tested our ability to use H3K4me3 in human Methamphetamine and Addiction prefrontal cortex from HIV+ individuals meeting criteria for methamphetamine use disorder or not Pathways in Postmortem HIV+ Brain (Meth +/−) which exhibited poor RNA quality and were not suitable for transcriptional profiling.
    [Show full text]
  • Systematic Detection of Brain Protein-Coding Genes Under Positive Selection During Primate Evolution and Their Roles in Cognition
    Downloaded from genome.cshlp.org on October 7, 2021 - Published by Cold Spring Harbor Laboratory Press Title: Systematic detection of brain protein-coding genes under positive selection during primate evolution and their roles in cognition Short title: Evolution of brain protein-coding genes in humans Guillaume Dumasa,b, Simon Malesysa, and Thomas Bourgerona a Human Genetics and Cognitive Functions, Institut Pasteur, UMR3571 CNRS, Université de Paris, Paris, (75015) France b Department of Psychiatry, Université de Montreal, CHU Ste Justine Hospital, Montreal, QC, Canada. Corresponding author: Guillaume Dumas Human Genetics and Cognitive Functions Institut Pasteur 75015 Paris, France Phone: +33 6 28 25 56 65 [email protected] Dumas, Malesys, and Bourgeron 1 of 40 Downloaded from genome.cshlp.org on October 7, 2021 - Published by Cold Spring Harbor Laboratory Press Abstract The human brain differs from that of other primates, but the genetic basis of these differences remains unclear. We investigated the evolutionary pressures acting on almost all human protein-coding genes (N=11,667; 1:1 orthologs in primates) based on their divergence from those of early hominins, such as Neanderthals, and non-human primates. We confirm that genes encoding brain-related proteins are among the most strongly conserved protein-coding genes in the human genome. Combining our evolutionary pressure metrics for the protein- coding genome with recent datasets, we found that this conservation applied to genes functionally associated with the synapse and expressed in brain structures such as the prefrontal cortex and the cerebellum. Conversely, several genes presenting signatures commonly associated with positive selection appear as causing brain diseases or conditions, such as micro/macrocephaly, Joubert syndrome, dyslexia, and autism.
    [Show full text]
  • Comprehensive Transcriptional Map of Primate Brain Development
    HHS Public Access Author manuscript Author ManuscriptAuthor Manuscript Author Nature. Manuscript Author Author manuscript; Manuscript Author available in PMC 2017 February 24. Published in final edited form as: Nature. 2016 July 21; 535(7612): 367–375. doi:10.1038/nature18637. Comprehensive transcriptional map of primate brain development A full list of authors and affiliations appears at the end of the article. Abstract The transcriptional underpinnings of brain development remain poorly understood, particularly in humans and closely related non-human primates. We describe a high resolution transcriptional atlas of rhesus monkey brain development that combines dense temporal sampling of prenatal and postnatal periods with fine anatomical parcellation of cortical and subcortical regions associated with human neuropsychiatric disease. Gene expression changes more rapidly before birth, both in progenitor cells and maturing neurons, and cortical layers and areas acquire adult-like molecular profiles surprisingly late postnatally. Disparate cell populations exhibit distinct developmental timing but also unexpected synchrony of processes underlying neural circuit construction including cell projection and adhesion. Candidate risk genes for neurodevelopmental disorders including primary microcephaly, autism spectrum disorder, intellectual disability, and schizophrenia show disease-specific spatiotemporal enrichment within developing neocortex. Human developmental expression trajectories are more similar to monkey than rodent, and approximately 9% of genes show human-specific regulation with evidence for prolonged maturation or neoteny. Users may view, print, copy, and download text and data-mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use:http://www.nature.com/authors/editorial_policies/license.html#terms Corresponding author: Ed S. Lein, Ph.D., Allen Institute for Brain Science, 615 Westlake Ave N, Seattle, WA 98109, Tel: 206.548.7039, Fax: 206.548.7071, [email protected].
    [Show full text]
  • Identification and Evolutionary Analysis of Potential Candidate Genes in a Human Eating Disorder
    Hindawi Publishing Corporation BioMed Research International Volume 2016, Article ID 7281732, 11 pages http://dx.doi.org/10.1155/2016/7281732 Research Article Identification and Evolutionary Analysis of Potential Candidate Genes in a Human Eating Disorder Ubadah Sabbagh, Saman Mullegama, and Gerald J. Wyckoff Molecular Biology and Biochemistry, University of Missouri-Kansas City, 5007 Rockhill Road, Room 521, Spencer Chemistry Building, Kansas City, MO 64110, USA Correspondence should be addressed to Gerald J. Wyckoff; [email protected] Received 15 December 2015; Revised 11 February 2016; Accepted 22 February 2016 Academic Editor: Luoying Zhang Copyright © 2016 Ubadah Sabbagh et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Thepurposeofthisstudywastofindgeneslinkedwitheatingdisordersandassociatedwithbothmetabolicandneuralsystems.Our operating hypothesis was that there are genetic factors underlying some eating disorders resting in both those pathways. Specifically, we are interested in disorders that may rest in both sleep and metabolic function, generally called Night Eating Syndrome (NES). A meta-analysis of the Gene Expression Omnibus targeting the mammalian nervous system, sleep, and obesity studies was performed, yielding numerous genes of interest. Through a text-based analysis of the results, a number of potential candidate genes were identified. VGF, in particular, appeared to be relevant both to obesity and, broadly, to brain or neural development. VGFisa highly connected protein that interacts with numerous targets via proteolytically digested peptides. We examined VGF from an evolutionary perspective to determine whether other available evidence supported a role for the gene in human disease.
    [Show full text]
  • Evolution of Cellular Diversity in Primary Motor Cortex of Human, Marmoset Monkey, and Mouse
    bioRxiv preprint doi: https://doi.org/10.1101/2020.03.31.016972; this version posted April 4, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. 1 Evolution of cellular diversity in primary motor cortex of human, marmoset monkey, and mouse 2 3 Trygve E. Bakken, Nikolas L. Jorstad, Qiwen Hu, Blue B. Lake, Wei Tian, Brian E. Kalmbach, Megan 4 Crow, Rebecca D. Hodge, Fenna M. Krienen, Staci A. Sorensen, Jeroen Eggermont, Zizhen Yao, Brian 5 D. Aevermann, Andrew I. Aldridge, Anna Bartlett, Darren Bertagnolli, Tamara Casper, Rosa G. 6 Castanon, Kirsten Crichton, Tanya L. Daigle, Rachel Dalley, Nick Dee, Nikolai Dembrow, Dinh Diep, 7 Song-Lin Ding, Weixiu Dong, Rongxin Fang, Stephan Fischer, Melissa Goldman, Jeff Goldy, Lucas T. 8 Graybuck, Brian R. Herb, Xiaomeng Hou, Jayaram Kancherla, Matthew Kroll, Kanan Lathia, Baldur van 9 Lew, Yang Eric Li, Christine S. Liu, Hanqing Liu, Jacinta D. Lucero, Anup Mahurkar, Delissa McMillen, 10 Jeremy A. Miller, Marmar Moussa, Joseph R. Nery, Philip R. Nicovich, Joshua Orvis, Julia K. Osteen, 11 Scott Owen, Carter R. Palmer, Thanh Pham, Nongluk Plongthongkum, Olivier Poirion, Nora M. Reed, 12 Christine Rimorin, Angeline Rivkin, William J. Romanow, Adriana E. Sedeño-Cortés, Kimberly Siletti, 13 Saroja Somasundaram, Josef Sulc, Michael Tieu, Amy Torkelson, Herman Tung, Xinxin Wang, 14 Fangming Xie, Anna Marie Yanny, Renee Zhang, Seth A. Ament, M. Margarita Behrens, Hector 15 Corrada Bravo, Jerold Chun, Alexander Dobin, Jesse Gillis, Ronna Hertzano, Patrick R.
    [Show full text]
  • Download The
    THE INTERPRETATION OF GENE COEXPRESSION IN SYSTEMS BIOLOGY by Marjan Farahbod MSc, Chalmers University of Technology, 2013 BSc, University of Tehran, 2010 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Bioinformatics) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) December 2019 Ó Marjan Farahbod, 2019 The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the dissertation entitled: The interpretation of gene coexpression in systems biology submitted by Marjan Farahbod in partial fulfillment of the requirements for the degree of Doctor of philosophy in Bioinformatics Examining Committee: Dr. Paul Pavlidis Supervisor Dr. Alexandre Bouchard Côté Supervisory Committee Member Dr. Joerg Gsponer Supervisory Committee Member Dr. Marco A. Marra University Examiner Dr. T. Michael Underhill University Examiner Additional Supervisory Committee Members: Dr. Wyeth Wasserman Supervisory Committee Member Supervisory Committee Member ii Abstract One of the key features of transcriptomic data is the similarity of expression patterns among groups of genes, referred to as coexpression. It has been shown that coexpressed genes tend to share similar functions. Based on this, a common assumption is that gene coexpression is a result of transcriptional regulation and therefore, regulatory relationships could be inferred from coexpression. However, success in inferring such relationships has been limited and there are questions about the source and interpretation of coexpression. Here I explore coexpression as an observed signal from the data, examine its source and assess its relevance for inferring regulatory relationships. In chapter 2 I studied differential coexpression, which refers to the alteration of gene coexpression between biological conditions.
    [Show full text]
  • Opalin (S-14): Sc-163189
    SAN TA C RUZ BI OTEC HNOL OG Y, INC . Opalin (S-14): sc-163189 BACKGROUND APPLICATIONS Opalin, also known as HTMP10, oligodendrocytic myelin paranodal and inner Opalin (S-14) is recommended for detection of Opalin of human origin by loop protein or TMEM10 (transmembrane protein 10), is a 141 amino acid Western Blotting (starting dilution 1:200, dilution range 1:100-1:1000), immu- single-pass type I membrane protein that is expressed specifically in oligo - nofluorescence (starting dilution 1:50, dilution range 1:50-1:500) and solid dendrocytes of brain. The gene encoding Opalin maps to human chromo some phase ELISA (starting dilution 1:30, dilution range 1:30-1:3000). 10q24.1. Spanning nearly 135 million base pairs, chromosome 10 makes up Suitable for use as control antibody for Opalin siRNA (h): sc-90734, Opalin approximately 4.5% of total DNA in cells and encodes nearly 1,200 genes. shRNA Plasmid (h): sc-90734-SH and Opalin shRNA (h) Lentiviral Particles: Several protein-coding genes, including those that encode for chemokines, sc-90734-V. cadherins, excision repair proteins, early growth response factors (Egrs) and fibroblast growth receptors (FGFRs), are located on chromosome 10. Defects Molecular Weight of Opalin: 16 kDa. in some of the genes that map to chromosome 10 are associated with Charcot- Molecular Weight of glycosylated Opalin: 34-39 kDa. Marie Tooth disease, Jackson-Weiss syndrome, Usher syndrome, nonsyndro - matic deafness, Wolman’s syndrome, Cowden syndrome, multiple endocrine RECOMMENDED SECONDARY REAGENTS neoplasia type 2 and porphyria. To ensure optimal results, the following support (secondary) reagents are REFERENCES recommended: 1) Western Blotting: use donkey anti-goat IgG-HRP: sc-2020 (dilution range: 1:2000-1:100,000) or Cruz Marker™ compatible donkey 1.
    [Show full text]